Sleep is essential for human health. Bad sleep and sleep disorders have been increasingly prevalent and are gradually becoming a social problem that cannot be ignored. The current gold standard in sleep monitoring is polysomnography (PSG) allowing nearly complete approach. Unfortunately, this wealth of information is obtained at the cost of invasive system, only usable in hospital environment under the control of sleep experts. Therefore, we develop a wireless body networks for home sleep monitoring with effort on non-intrusiveness, portability and autonomy. In this paper, we present our global architecture from sensors to user display with a focus on main functions and hardware. Then, we introduce the chosen indicators for sleep monitoring and the algorithms developed for sleep stages classification. Finally we show the evaluation of our approach compared to PSG. We illustrate the sleep stage classification during one night in the sleep unit of Toulouse University Hospital and highlight correlation between body temperature on extremities and Periodic Limb Movement during Sleep. Results are promising but need to be reinforced with new tests in hospital with several volunteers